The present invention relates to a simulator or a CAD (Computer Aided Design) system utilized for product design in a variety of scientific technological fields such as design of semiconductor devices, integrated circuits and magnetic storage devices. More particularly, the present invention relates to a method for executing a parameter survey based on simulation to realize optimum product design and a reduction in product design schedule.
In many applications of the product design, a series of working operations of setting parameters representing design objects, making a prototype of a product actually on trial, and evaluating the performance of the product are repeated in a trial and error fashion.
As shown in FIG. 7, values of parameters (Pa, Pb, . . . , Pm) are set on the basis of the past experimental results and experiences in block 701, and a prototype of a product is actually manufactured for trial in accordance with the parameters.
For example, the prototype of the product is manufactured on trial in accordance with parameter 1 (Pa1, Pb1, . . . , Pm1), parameter 2 (Pa1, Pb1, . . . , Pm3), . . . . . . , parameter 4 (Pa4, Pb1, , Pm1), . . . .
Next, in block 702, the performance of the product manufactured for trial in accordance with the individual parameters is measured to obtain experimental results (D1, D2, D3, D4, . . . ) in block 703. Here, D1 indicates the experimental result for the product according to parameter 1. Similarly, this holds true for D2 and so forth.
One or more experimental results satisfying design condition 704 are selected from the experimental results 703, and the selected experimental results are set as design values.
In block 705, design value D2 (Pa1, Pb1, . . . , Pm3) and design value D4 (Pa4, Pb1, . . . , Pm1) are obtained.
If any experimental results satisfying the design condition cannot be obtained, the values of parameters representing design objects are newly set again, the product is manufactured for trial again, and the measurement is conducted. This procedure is repeated until the design values satisfying the design condition can be obtained.
In this simple design flow, since the kind and number of parameters representing design objects are infinite, much time and efforts are needed and high costs are incurred.
As a method for reduction of iteration time of trial manufacture, it is proposed to add disturbance in Taguchi method in order to evaluate the function (Nikkei Mechanical, Feb. 19, 1996, No. 474, pp. 30-33), for example.
According to Taguchi method, the kinds of parameters are separated into control parameters which can actually be controlled and designed, and error parameters which cannot be controlled.
In the control parameters, a set of the minimum number of parameters is set using cross tables utilized in the statistic method.
For example, 18 sets of cross tables are recommended to reduce the number of parameters.
Next, a product is actually manufactured on trial using the sets of the minimum number of parameters according to the cross tables, and the S/N ratio and sensitivity are measured as characteristics of the trial product.
In the procedure of setting the values of parameters, a set of parameters with high S/N ratio is first selected, and then a set of parameters with high sensitivity is selected. Thereby, a product with a highly stability is designed.
In the error parameters, however, a great number of combinations, being equal in number to the kind and number of parameters, take place.
Thus, the trial manufacture and measurement must be repeated such a great number of times. Accordingly, the number of trial manufactures must further be reduced to overcome the problem of high costs.
Under the circumstances, a method for parallel parameter survey has been proposed (JP-A-11-338829 entitled “Job and Data Managing Method”). In this method, simulation is executed on a parallel machine or on a numerical value operation unit of work station cluster to simultaneously calculate values of parameters representing design objects on individual processors.
A user sets values of parameters in material and structure modeling, physical modeling, numerical value experiment modeling of numerical value calculation method and so forth through an input display unit which includes a mouse, a keyboard, a display and so forth.
Based on the values of these input parameters, a computer comprised of a CPU (Central Processing Unit), memories and a network executes an operation process based on a simulation program.
The execution results of the simulation are output to an output display unit such as a display in a data or graphical form to support the user in product design.
To describe the above method more specifically by making reference to FIG. 8, values of input parameters (Pa, Pb, . . . , Pm) representing design objects are set in block 801, the values of the individual input parameters are simultaneously executed in parallel on the individual processors of the parallel machine or work station cluster connected to the network in block 802 instead of sequentially executing the simulation, and simulation results (D1, D2, D3, D4, . . . , Dk) are obtained at a high speed by the number of processors in block 803.
If the simulation execution results 803 do not satisfy design condition 804, the parameters are set again in block 801, and the parallel parameter survey is repetitively executed in block 802 until design value D1 (Pa1, Pb3, . . . , Pm4) and design value D3 (Pa4, Pb2, . . . , Pm2) satisfying the design condition 804 can be obtained in block 805.
The above parallel parameter survey method can reduce the iteration time of actual trial manufacture to a great extent to solve the problem of high costs, and can reduce the time required for simulation calculation by the number of processors.
In recent years, user's needs have been diversified more and more, and reduction of development cycle of products and earlier bringing products to market are indispensable for assuring high profit ratios.
To this end, in the product design based on simulation, reduction of the kind and number of input parameters representing design objects is indispensable.
For example, when there are four kinds of input parameters and there are four recommended parameter values for the individual input parameters in block 802 in FIG. 8, a set of 256 (=44) parameters take place, and time required for calculation in simulation increases very drastically even when the parallel parameter survey method is employed.
Further, as the kind of input parameter and the number of input values of parameters increase, setting of the values of input parameters must be repeated through the manual working operation. Therefore, there is a high possibility that the input values of parameters are mistaken owing to artificial mistakes.
In addition, it is very difficult for a user and customer to change from the conventionally accustomed product design based on trial manufacture to the product design based on the simulation using the parameter survey.
There needs guidance to how to combine the conventional experimental results and analytical results with the execution results of simulation, and how to search the values of parameters satisfying the design condition.
Build-up for quickly and smoothly changing to the product design based on the simulation must be supported.
Accordingly, it is important in the product design based on the simulation to provide a framework of product design in which the kind and number of input parameters representing design objects can be reduced to a great extent, artificial mistakes of input values of parameters due to manual working operations can be prevented, and the simulation permitting optimum design at low costs and at high speeds can be used in place of the actual trial manufacture while using the conventional experimental results and the results of the simulation in combination.
It is desirable that the optimum design of products is drastically accelerated by preparing a virtual design database in which values of input parameters and results of simulation are stored by building-up this simulation framework rapidly.
For example, realization of a simulation framework for quality improvement design can be expected in which the sensitivity and correlation coefficients of the operation margin relative to the process margin can be calculated, the yield of products can be built up rapidly, and the time for countermeasures against defective design can be reduced.